Eksplorasi Pengelompokan Kota-Kota di Indonesia Berdasarkan Tingkat Inflasi Menggunakan Algoritma Clustering K-Means

Abstract

Inflation is one of the crucial indicators in a country's economy, including Indonesia. To measure the inflation rate, an indicator called the Consumer Price Index (CPI) is used. The CPI is an index that calculates the average price changes of a basket of goods and services generally consumed by the public over a specific period. The K-Means method is a data clustering technique that groups data into one or more clusters, where data with similar characteristics are grouped into the same cluster, while data with different characteristics are separated into other clusters. This study aims to apply the K-Means clustering algorithm to identify various inflation rate characteristics based on the Consumer Price Index in 90 cities across Indonesia. The final result of cluster formation in this research is the establishment of five optimal clusters after going through iterations, namely: Cluster 1 Cities with very high inflation rates, Cluster 2 Cities with high inflation rates, Cluster 3 Cities with medium inflation rates, Cluster 4 Cities with low inflation rates, and Cluster 5 Cities with very low inflation rates.